US8265172B2 - Method and apparatus for analytical and empirical hybrid encoding distortion modeling - Google Patents

Method and apparatus for analytical and empirical hybrid encoding distortion modeling Download PDF

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US8265172B2
US8265172B2 US12/310,460 US31046007A US8265172B2 US 8265172 B2 US8265172 B2 US 8265172B2 US 31046007 A US31046007 A US 31046007A US 8265172 B2 US8265172 B2 US 8265172B2
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distortion
video encoding
zero quantized
quantized coefficient
frame
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Hua Yang
Jill MacDonald Boyce
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Thomson Licensing SAS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
    • H04N19/86—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/103—Selection of coding mode or of prediction mode
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/115—Selection of the code volume for a coding unit prior to coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/146—Data rate or code amount at the encoder output
    • H04N19/147—Data rate or code amount at the encoder output according to rate distortion criteria
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/154—Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
    • H04N19/61—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding in combination with predictive coding

Definitions

  • R or D are represented as an explicit function with respect to the quantization scale Q and the variance of the residue signal ⁇ 2 .
  • an apparatus includes a distortion calculator for modeling video encoding distortion by dividing the video encoding distortion into a first portion and a second portion, calculating the first portion using empirical calculations, and calculating the second portion using analytical calculations.
  • the method includes modeling video encoding distortion.
  • the step of modeling the video encoding distortion includes the steps of dividing the video encoding distortion into a first portion and a second portion, calculating the first portion using empirical calculations, and calculating the second portion using analytical calculations.
  • FIG. 1 is a flow diagram for an exemplary method relating to a hybrid distortion model, in accordance with an embodiment of the present principles
  • processor or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (“DSP”) hardware, read-only memory (“ROM”) for storing software, random access memory (“RAM”), and non-volatile storage.
  • DSP digital signal processor
  • ROM read-only memory
  • RAM random access memory
  • FIG. 1 an exemplary method relating to a hybrid distortion model is indicated generally by the reference numeral 100 .
  • rate-distortion modeling In the basic rate-distortion modeling problem, it is commonly presumed that the input signal to transformation, quantization and entropy coding is available, and the task of rate-distortion modeling is to estimate the rate-distortion outcomes of applying different QPs onto this input signal.
  • the concerned input signal is the residue signal after motion compensated prediction or intra-prediction.
  • rate-distortion models in practical problems, one usually may not know the exact input signal prior to transform coding. For example, in the problem of frame-level bit allocation, one has to estimate the rate-distortion data of all the concerned frames without coding any one of them.
  • the resultant mean squared error distortion D(Q) is divided into two parts: distortion contribution of non-zero quantized coefficients D nz (Q) and that of zero quantized coefficients D z (Q).
  • the concerned distortion is usually distortion of the luminance component only.
  • luminance distortion we also refer to luminance distortion.
  • the proposed model applies as well to distortion involving both the luminance and chrominance components.
  • we ignore the clipping impact and presume that the distortion in the frequency domain is the same as that in the spatial domain. Hence, we have the following:
  • f i and ⁇ circumflex over (f) ⁇ i denote the original and reconstructed pixels of the frame, and A denotes the total number of pixels in a frame.
  • QP ranges from 0 to 51, and the relationship between QP and Q is roughly as follows: Q ⁇ 2 (QP-4)/6 .
  • ⁇ (Q)de notes the percentage of zero quantized coefficients among all the transform coefficients of the frame, which has a one-to-one mapping with Q.
  • the distortion of zero quantized coefficients is exactly calculated as follows:
  • Coeff z (Q) denotes the magnitude of a coefficient that will be quantized to zero with quantization scale Q.
  • the overall source coding distortion is estimated as follows:
  • the proposed model is a hybrid solution, where an analytic function is assumed for non-zero coefficient distortion, and for zero coefficients, their exact distortion contribution is calculated. It is to be noted that presuming uniform distribution for non-zero coefficient quantization error, and calculating the as-is distortion for zero coefficients, have been separately employed in source coding distortion modeling. However, unlike our proposed model, existing solutions all apply either one of the two practices in estimating the overall source coding distortion, depending on the different values of relative Q versus ⁇ magnitude, which thus leads to various piecewise analytic distortion models.
  • our model In terms of computational complexity, similar to the existing ⁇ -domain models, our model also estimates the distortion in the transform domain. Thus, it requires a one time transform operation, which only incurs a marginal complexity increase. Especially, in the MPEG-4 AVC Standard, the adopted transform is an approximation to the original Discrete Cosine Transform, which involves lower computational complexity.
  • a complexity that may be implicated by our model relates to the calculation of the percentage and distortion of the zero quantized coefficients. In the worst case, for each Q, one has to conduct quantization on all the transform coefficients, and exhaustively count the number and distortion of zero quantized coefficients, which may require significant computational complexity.
  • GOP group-of-picture
  • the first frame of each GOP will be coded as an I-frame.
  • I-frame the first frame of each GOP
  • FIG. 2 and FIG. 3 for simplicity and reduced complexity, only Inter16 ⁇ 16 mode is presumed in pre-analysis.
  • the present principles are not limited solely to Inter16 ⁇ 16 mode and, thus, other modes may also be used, while maintaining the spirit of the present principles.
  • quantization may be applied to generate an approximated encoder reconstruction frame for reference, where the quantization parameter (QP) could be some average QP of the last encoded GOP.
  • Past encoding results i.e., R i-1,actual and D i-1,actual
  • R i-1,actual and D i-1,actual can be used to adaptively update parameters in the R and D models, e.g., the parameter ⁇ as for the proposed D model of Equation (6).
  • the estimated D-Q data via the proposed hybrid distortion model can be applied in several ways to optimize frame-level bit allocation. For example, considering all the remaining frames and satisfying the constraint of the remaining total bit budget, optimal bit allocation is commonly defined by either minimizing the average distortion or minimizing the maximum distortion of the remaining frames.
  • the allocated bit budget of a frame is then sent to MB-level rate control module, which will finally determine an appropriate QP for each macroblock (MB) and is meant to accurately achieve the allocated bit budget. This is illustrated in FIG. 5 .
  • An input of the combiner 305 and an input of the motion estimator 350 are available as inputs to the pre-analyzer 300 , for receiving input video frames.
  • An output of the frame level ⁇ -Q data and D-Q data calculator 320 is available as an output of the pre-analyzer 300 , for outputting frame-level rate control data.
  • the frame-level rate controller 400 includes a first updater 405 having an output in signal communication with a first input of a frame-level bit allocator 410 .
  • the frame-level rate controller 400 further includes a second updater 415 having an output connected in signal communication with a second input of the frame-level bit allocator 410 .
  • a second input of the second updater 415 is available as an input of the frame-level rate controller 400 , for receiving D i-1,actual .
  • a third input of the second updater 415 is available as an input of the frame-level rate controller 400 , for receiving estimated values for R-Q and D-Q data, for example, from the pre-analyzer 300 of FIG. 3 .
  • An output of the frame-level bit allocator 410 is available as an output of the frame-level rate controller 400 , for outputting R i,allocated .
  • a second output of the quantizer 515 is connected in signal communication with a first input of the inverse quantizer 520 .
  • An output of the inverse quantizer 520 is connected in signal communication with an input of an inverse transformer 525 .
  • An output of the inverse transformer 525 is connected in signal communication with a first non-inverting input of a combiner 530 .
  • An output of the combiner 530 is connected in signal communication with a second input of a frame-level actual encoder distortion calculator 550 and an input of a reference picture buffer 535 .
  • An output of the reference picture buffer 535 is connected in signal communication with a second input of a motion estimator and coding mode selector 540 .
  • another advantage/feature is the apparatus having the distortion calculator that divides the video encoding distortion by assigning zero quantized coefficient distortion for the first portion and assigning non-zero quantized coefficient distortion for the second portion as described above, wherein the zero quantized coefficient distortion is exactly calculated.
  • another advantage/feature is the apparatus having the distortion calculator that divides the video encoding distortion by assigning zero quantized coefficient distortion for the first portion and assigning non-zero quantized coefficient distortion for the second portion as described above, wherein the distortion calculator calculates values of the zero quantized coefficient distortion for all quantization step sizes using a one-pass look-up over all zero quantized coefficients.
  • another advantage/feature is the apparatus having the distortion calculator that divides the video encoding distortion by assigning zero quantized coefficient distortion for the first portion and assigning non-zero quantized coefficient distortion for the second portion as described above, wherein the non-zero quantized coefficient distortion is estimated using a random variable with uniform distribution.
  • Another advantage/feature is the apparatus having the distortion calculator as described above, wherein the video encoding distortion is a source coding mean squared error distortion.
  • the teachings of the present principles are implemented as a combination of hardware and software.
  • the software may be implemented as an application program tangibly embodied on a program storage unit.
  • the application program may be uploaded to, and executed by, a machine comprising any suitable architecture.
  • the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPU”), a random access memory (“RAM”), and input/output (“I/O”) interfaces.
  • CPU central processing units
  • RAM random access memory
  • I/O input/output
  • the computer platform may also include an operating system and microinstruction code.
  • the various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU.
  • various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit.

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US9491476B2 (en) 2013-07-05 2016-11-08 Samsung Electronics Co., Ltd. Method and apparatus for deciding a video prediction mode

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EP2544450B1 (en) * 2011-07-07 2016-04-13 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Model parameter estimation for a rate- or distortion-quantization model function
JP6145069B2 (ja) * 2014-04-30 2017-06-07 日本電信電話株式会社 主観画質推定装置及び主観画質推定プログラム

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090080518A1 (en) * 2007-07-20 2009-03-26 Oscar Chi Lim Au Rate control and video denoising for noisy video data
US8982947B2 (en) * 2007-07-20 2015-03-17 The Hong Kong University Of Science And Technology Rate control and video denoising for noisy video data
US9491476B2 (en) 2013-07-05 2016-11-08 Samsung Electronics Co., Ltd. Method and apparatus for deciding a video prediction mode

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JP5087624B2 (ja) 2012-12-05
WO2008027250A3 (en) 2008-05-22
EP2060125B1 (en) 2011-04-06
US20090232225A1 (en) 2009-09-17
CN101513072A (zh) 2009-08-19
KR20090057236A (ko) 2009-06-04
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JP2010503265A (ja) 2010-01-28
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